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	Part #	Description	CATEGORY	SUB CATEGORY	Camera Type	Resolution [MPixel]	Width [Pixel]	Height [Pixel]	FrameRate [f/s]	Sensor Format	Pixel W[µm]	Pixel H[µm]	Interface	Sensor Type	Shutter Type	Bit_Depth	MANUFACTURER	Size	Datasheet	Sensor Manufacturer	Sensor Model	Full well capacity	Type	Auto Iris	Mount	Voltage	Temp. Range	Weight[g]	Power	Illumination Color	HDMI Output	Line Rate [Kline/s]	Pixel Rate (MHz)	Host Interface	OS Support	Programmable FPGA	F#	MOD	Focus Type	Iris Type	Zoom Type	Opt. Mag.	DRAWING	NA	IF Side1	IF Side2	Freq range [MHz]	PoCL	Number of Channels	Environmental	Max Current Continuous	Max Current Pulsed	INTERFACING	Minimum Pulse Width (μS)	SafeSense	SafePower	Max. Memory	Processor Speed	Control	USERMANUAL	Co-axlight	MaxBitrate	ColorMono	Discontinued Product	Sensor Format Range	Frame_Rate_Range	Resolution_Range	Focal_Length_range	Focal_Length
	Part #	Description	CATEGORY	SUB CATEGORY	Camera Type	Resolution [MPixel]	Width [Pixel]	Height [Pixel]	FrameRate [f/s]	Sensor Format	Pixel W[µm]	Pixel H[µm]	Interface	Sensor Type	Shutter Type	Bit_Depth	MANUFACTURER	Size	Datasheet	Sensor Manufacturer	Sensor Model	Full well capacity	Type	Auto Iris	Mount	Voltage	Temp. Range	Weight[g]	Power	Illumination Color	HDMI Output	Line Rate [Kline/s]	Pixel Rate (MHz)	Host Interface	OS Support	Programmable FPGA	F#	MOD	Focus Type	Iris Type	Zoom Type	Opt. Mag.	DRAWING	NA	IF Side1	IF Side2	Freq range [MHz]	PoCL	Number of Channels	Environmental	Max Current Continuous	Max Current Pulsed	INTERFACING	Minimum Pulse Width (μS)	SafeSense	SafePower	Max. Memory	Processor Speed	Control	USERMANUAL	Co-axlight	MaxBitrate	ColorMono	Discontinued Product	Sensor Format Range	Frame_Rate_Range	Resolution_Range	Focal_Length_range	Focal_Length

Support

Runtime

For typical requirements - fast processing and convenient usage we recommend to use NVIDIA GPU with enough memory. A rough estimation is 3GB per deep-learning module. To be on the safe side we recommend to use NVIDIA GeForce® RTX 2080 Ti.
For price sensitive use-cases it is possible to use cheaper NVIDIA GPU card or to use a computer without a GPU card at all. It has to be expected longer recognition times.
For embedded use-cases we support embedded hardware too - e.g. ARM based devices like NVIDIA TX2 or Xavier or some types of FPGA. Contact us for more information.

Development

For training of deep-learning modules we recommend to use fast NVIDIA GPU with a lot of memory. We recommend to use NVIDIA GeForce® RTX 2080 Ti. Plus we recommend a PC with at least 16 GB of RAM.
Training of deep-learning modules without GPU technically works but we strongly discourage from this as the training times can take hours or even days.

Do you need a GPU?

If you use or train modules of Anomaly of Surface or Surface Detection you can use a PC without GPU.
If you use modules for Images Preprocessing, Measurement or Code you can safely use PC without GPU.
If you are going to use modules for Classification of Object Detection we strongly recommend to use GPU for training. It is possible to use PC without GPU for image recognition (inference) however the inference times would be significantly higher.

HOW TO INTEGRATE PEKAT VISION

Camera is directly connected to PEKAT VISION (GenICam camera)

This option is the easiest. PEKAT VISION processes input from the camera in a loop. You have to enable the camera and set what should happen after evaluation (output).

PEKAT VISION is middleware

You use additional software for capturing images or processing. This method has 3 steps and requires programming knowledge.

Send images to PEKAT
- You can use SDK from https://github.com/pekat-vision/. We support Python, C# and C++ programming languages.
- You can use LabVIEW plugin http://sine.ni.com/nips/cds/view/p/lang/en/nid/218329
- You can send images to HTTP API. We created some examples.
Pekat processes the image. It returns data structure (JSON) which describes the result.
Your script from step 1 processes the response. In the response, there could be information about objects which were found or overall evaluation. You can use the inspection tab for debugging.